There is currently an overload of available choices for various items like movies, restaurants, books, mobile phone apps, tweets, news articles, etc. This invariably has led to the difficulty of discovering the best items from the astronomical number of available options. Of the various tools available to help people discover the best items, top-selling/top-rated lists are the most popular; examples include NY Times best-selling books, Zagat top-rated restaurants in a city, Yelp top-rated restaurants in a year, best-selling apps in the App store, etc. The lists are determined either using the sales or the ratings of the different items. The top-selling/rated lists reflect the preferences of the people and operate on the premise that a “typical” user would like a popular item. In addition, they can be treated as a democratic and objective way to determine the “best” items.
The main reason top-selling/rated lists are popular is that they are very intuitive and simple. In addition, they need only aggregate level ratings or sales data. This is in contrast to more complex discovery tools such as personalized recommendation engines or top-ten critics' lists (for books or movies), which require data that is difficult to obtain either at the individual level (likes of the user in the form of ratings) or at the item level (the critics should have seen the movie or read the book).
Despite their popularity, the top-selling/rated lists suffer from various limitations. The main limitation is that they fail to capture the subtleties of user preferences because they use only one ranking to reflect the preferences of the entire population. In other words, they cater only to the “typical” or the “mainstream” user. The obvious issue with using only one ranked list to cater to only the “typical” user is that (a) not every user is “typical”, and (b) a typical user is not always “typical.” More specifically, consider the top-rated restaurants filtered by say location that is generated by a restaurant-ratings site such as Yelp/Zagat. In this case, firstly, the top-rated restaurants might fail to capture the preferences of the user whose tastes may not be mainstream. Secondly, the tastes of the same user vary with the time or mood of the user. For example, a user with mainstream tastes at one time might want to experiment with not so mainstream restaurants depending on the mood.
Given the above limitation, a single top-selling/rated list is not sufficiently reflective of the preferences of the population. A popular solution to this problem is to make personalized recommendations. However, this is not a viable solution in many situations in practice because there may not be sufficient individual-level data to make personalized recommendations. For example many users visiting Yelp or Zagat have never rated a single restaurant.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.